R Notebook

Run download_data.Rmd and percentage_of_regional_richness.Rmd First!

merlin_city_data <- fetch_city_data_for('merlin')

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  name = col_character(),
  response = col_double()
)

Joining, by = "name"

merlin_city_data

library(tidyverse)

Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ tibble  3.1.2     ✓ dplyr   1.0.7
✓ tidyr   1.1.3     ✓ stringr 1.4.0
✓ readr   1.4.0     ✓ forcats 0.5.1
✓ purrr   0.3.4     
── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::combine()  masks randomForest::combine()
x dplyr::filter()   masks stats::filter()
x dplyr::lag()      masks stats::lag()
x ggplot2::margin() masks randomForest::margin()

merlin_city_data_fixed <- rfImpute(response ~ ., merlin_city_data)

     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    16.93    93.94 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    17.06    94.62 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    16.88    93.63 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    16.94    93.98 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    16.62    92.18 |

merlin_city_data_fixed

randomForest(response ~ ., merlin_city_data_fixed)

Call:
 randomForest(formula = response ~ ., data = merlin_city_data_fixed) 
               Type of random forest: regression
                     Number of trees: 500
No. of variables tried at each split: 17

          Mean of squared residuals: 16.40603
                    % Var explained: 8.99

select_variables_from_random_forest(merlin_city_data_fixed)

 [1] "merlin_pool_size"                                        "realm"                                                   "biome_name"                                             
 [4] "rainfall_monthly_min"                                    "temperature_annual_average"                              "happiness_positive_effect"                              
 [7] "region_20km_elevation_delta"                             "percentage_urban_area_as_open_public_spaces"             "region_20km_urban"                                      
[10] "region_50km_elevation_delta"                             "temperature_monthly_min"                                 "region_20km_cultivated"                                 
[13] "permanent_water"                                         "region_50km_urban"                                       "region_100km_cultivated"                                
[16] "shrubs"                                                  "city_gdp_per_population"                                 "region_50km_cultivated"                                 
[19] "region_100km_elevation_delta"                            "happiness_negative_effect"                               "region_100km_urban"                                     
[22] "region_50km_average_pop_density"                         "region_20km_average_pop_density"                         "share_of_population_within_400m_of_open_space"          
[25] "rainfall_annual_average"                                 "city_average_pop_density"                                "herbaceous_wetland"                                     
[28] "temperature_monthly_max"                                 "region_100km_average_pop_density"                        "city_mean_elevation"                                    
[31] "mean_population_exposure_to_pm2_5_2019"                  "rainfall_monthly_max"                                    "happiness_future_life"                                  
[34] "city_max_pop_density"                                    "region_50km_mean_elevation"                              "cultivated"                                             
[37] "region_20km_mean_elevation"                              "region_100km_mean_elevation"                             "urban"                                                  
[40] "population_growth"                                       "percentage_urban_area_as_open_public_spaces_and_streets" "open_forest"                                            
[43] "percentage_urban_area_as_streets"                        "closed_forest"                                          

select_variables_from_random_forest(merlin_city_data_fixed_single_scale)

 [1] "merlin_pool_size"                              "realm"                                         "biome_name"                                   
 [4] "temperature_annual_average"                    "happiness_positive_effect"                     "region_20km_elevation_delta"                  
 [7] "percentage_urban_area_as_open_public_spaces"   "rainfall_monthly_min"                          "permanent_water"                              
[10] "temperature_monthly_min"                       "region_20km_urban"                             "shrubs"                                       
[13] "region_20km_cultivated"                        "happiness_negative_effect"                     "share_of_population_within_400m_of_open_space"
[16] "temperature_monthly_max"                       "rainfall_monthly_max"                          "rainfall_annual_average"                      
[19] "happiness_future_life"                         "city_max_pop_density"                          "city_mean_elevation"                          
[22] "city_elevation_delta"                          "cultivated"                                    "population_growth"                            
[25] "region_50km_mean_elevation"                    "percentage_urban_area_as_streets"              "open_forest"                                  
[28] "closed_forest"                                

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size")])

[1] "Mean  18.3605779376349 , SD:  0.199971871406664 , Mean + SD:  18.5605498090416"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm")])

[1] "Mean  13.8483857782536 , SD:  0.167233775821336 , Mean + SD:  14.015619554075"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name")])

[1] "Mean  14.1417551442684 , SD:  0.160688673425095 , Mean + SD:  14.3024438176934"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average")])

[1] "Mean  14.5103292900374 , SD:  0.234962153093279 , Mean + SD:  14.7452914431307"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect")])

[1] "Mean  14.7574657636192 , SD:  0.243199251750122 , Mean + SD:  15.0006650153693"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta")])

[1] "Mean  14.9256981639764 , SD:  0.240704529916054 , Mean + SD:  15.1664026938924"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces")])

[1] "Mean  14.788101572417 , SD:  0.263257912139604 , Mean + SD:  15.0513594845566"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min")])

[1] "Mean  14.708125023514 , SD:  0.244893814930232 , Mean + SD:  14.9530188384442"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water")])

[1] "Mean  14.71026136053 , SD:  0.251430414275647 , Mean + SD:  14.9616917748057"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min")])

[1] "Mean  15.0471745956683 , SD:  0.215420444110583 , Mean + SD:  15.2625950397789"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban")])

[1] "Mean  15.1350022423199 , SD:  0.339891929354689 , Mean + SD:  15.4748941716745"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs")])

[1] "Mean  15.1353707728019 , SD:  0.319439216090642 , Mean + SD:  15.4548099888925"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated")])

[1] "Mean  15.169974474564 , SD:  0.270058572377652 , Mean + SD:  15.4400330469417"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect")])

[1] "Mean  15.2117893322605 , SD:  0.300179792841188 , Mean + SD:  15.5119691251017"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space")])

[1] "Mean  15.2800537211106 , SD:  0.287378331903091 , Mean + SD:  15.5674320530137"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "temperature_monthly_max")])

[1] "Mean  15.4080077907785 , SD:  0.299436746178816 , Mean + SD:  15.7074445369573"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "temperature_monthly_max", "rainfall_monthly_max")])

[1] "Mean  15.6416908542101 , SD:  0.24366735672925 , Mean + SD:  15.8853582109394"

create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "temperature_monthly_max", "rainfall_monthly_max", "rainfall_annual_average")])

[1] "Mean  15.6107006616448 , SD:  0.240063905776268 , Mean + SD:  15.850764567421"

“merlin_pool_size”, “realm”

birdlife_city_data <- fetch_city_data_for('birdlife')

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  name = col_character(),
  response = col_double()
)

Joining, by = "name"

birdlife_city_data

birdlife_city_data_fixed <- rfImpute(response ~ ., birdlife_city_data)

     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    5.508    87.19 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    5.356    84.79 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    5.557    87.96 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    5.547    87.82 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    5.401    85.50 |

birdlife_city_data_fixed

select_variables_from_random_forest(birdlife_city_data_fixed)

 [1] "population_growth"                                       "birdlife_pool_size"                                      "region_100km_cultivated"                                
 [4] "biome_name"                                              "region_20km_average_pop_density"                         "rainfall_monthly_min"                                   
 [7] "percentage_urban_area_as_open_public_spaces"             "region_50km_cultivated"                                  "permanent_water"                                        
[10] "region_50km_average_pop_density"                         "rainfall_monthly_max"                                    "mean_population_exposure_to_pm2_5_2019"                 
[13] "temperature_monthly_min"                                 "shrubs"                                                  "temperature_annual_average"                             
[16] "region_100km_average_pop_density"                        "region_100km_urban"                                      "region_20km_cultivated"                                 
[19] "percentage_urban_area_as_open_public_spaces_and_streets" "region_20km_elevation_delta"                             "share_of_population_within_400m_of_open_space"          
[22] "region_20km_urban"                                       "city_average_pop_density"                                "happiness_future_life"                                  
[25] "region_50km_elevation_delta"                             "region_50km_urban"                                       "open_forest"                                            
[28] "percentage_urban_area_as_streets"                        "temperature_monthly_max"                                 "realm"                                                  
[31] "city_max_pop_density"                                    "city_elevation_delta"                                    "rainfall_annual_average"                                
[34] "city_gdp_per_population"                                 "cultivated"                                              "happiness_negative_effect"                              
[37] "region_100km_mean_elevation"                             "region_50km_mean_elevation"                              "city_mean_elevation"                                    
[40] "closed_forest"                                           "happiness_positive_effect"                               "herbaceous_wetland"                                     
[43] "urban"                                                   "herbaceous_vegetation"                                  

select_variables_from_random_forest(birdlife_city_data_fixed_single_scale)

 [1] "population_growth"                                       "birdlife_pool_size"                                      "region_100km_cultivated"                                
 [4] "percentage_urban_area_as_open_public_spaces"             "biome_name"                                              "rainfall_monthly_min"                                   
 [7] "region_20km_average_pop_density"                         "permanent_water"                                         "rainfall_monthly_max"                                   
[10] "temperature_annual_average"                              "temperature_monthly_min"                                 "mean_population_exposure_to_pm2_5_2019"                 
[13] "region_100km_urban"                                      "shrubs"                                                  "region_20km_elevation_delta"                            
[16] "percentage_urban_area_as_open_public_spaces_and_streets" "share_of_population_within_400m_of_open_space"           "realm"                                                  
[19] "city_average_pop_density"                                "open_forest"                                             "happiness_future_life"                                  
[22] "city_elevation_delta"                                    "temperature_monthly_max"                                 "rainfall_annual_average"                                
[25] "percentage_urban_area_as_streets"                        "city_gdp_per_population"                                 "cultivated"                                             
[28] "happiness_negative_effect"                               "closed_forest"                                           "region_50km_mean_elevation"                             
[31] "city_mean_elevation"                                     "herbaceous_wetland"                                      "urban"                                                  
[34] "herbaceous_vegetation"                                  

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth")])

[1] "Mean  6.35474438479356 , SD:  0.0712540254137255 , Mean + SD:  6.42599841020729"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size")])

[1] "Mean  5.53431593845832 , SD:  0.0808938134022798 , Mean + SD:  5.6152097518606"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated")])

[1] "Mean  5.03645460544453 , SD:  0.080993921046237 , Mean + SD:  5.11744852649077"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name")])

[1] "Mean  5.01181482503518 , SD:  0.0829095109876754 , Mean + SD:  5.09472433602285"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min")])

[1] "Mean  4.97549471316348 , SD:  0.0685833826522881 , Mean + SD:  5.04407809581577"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density")])

[1] "Mean  4.86486157758304 , SD:  0.0978544677155927 , Mean + SD:  4.96271604529863"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water")])

[1] "Mean  4.75503603367959 , SD:  0.0820218992111515 , Mean + SD:  4.83705793289075"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max")])

[1] "Mean  4.83291787856041 , SD:  0.0877474150947955 , Mean + SD:  4.9206652936552"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average")])

[1] "Mean  4.89459108748752 , SD:  0.0675800594452371 , Mean + SD:  4.96217114693276"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min")])

[1] "Mean  4.87244265396031 , SD:  0.0892281563604926 , Mean + SD:  4.9616708103208"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019")])

[1] "Mean  4.83199994339927 , SD:  0.0822602681223139 , Mean + SD:  4.91426021152158"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban")])

[1] "Mean  4.82180678041379 , SD:  0.0733987377154572 , Mean + SD:  4.89520551812925"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs")])

[1] "Mean  4.88760637080854 , SD:  0.0708285469155194 , Mean + SD:  4.95843491772406"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta")])

[1] "Mean  4.92024268589129 , SD:  0.102386290093399 , Mean + SD:  5.02262897598469"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets")])

[1] "Mean  4.91143919034019 , SD:  0.0837468464163517 , Mean + SD:  4.99518603675654"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space")])

[1] "Mean  4.96416771734588 , SD:  0.0825668458432887 , Mean + SD:  5.04673456318917"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "realm")])

[1] "Mean  4.98107377749941 , SD:  0.0814576329581648 , Mean + SD:  5.06253141045758"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "realm", "city_average_pop_density")])

[1] "Mean  4.9987069406936 , SD:  0.0742949984686252 , Mean + SD:  5.07300193916222"

create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "realm", "city_average_pop_density", "open_forest", "happiness_future_life")])

[1] "Mean  5.02149734174997 , SD:  0.0854499582879466 , Mean + SD:  5.10694730003792"

“population_growth”, “birdlife_pool_size”, “region_100km_cultivated”, “percentage_urban_area_as_open_public_spaces”, “biome_name”, “rainfall_monthly_min”, “region_20km_average_pop_density”, “permanent_water”

either_city_data <- fetch_city_data_for('either')

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  name = col_character(),
  response = col_double()
)

Joining, by = "name"

either_city_data

either_city_data_fixed <- rfImpute(response ~ ., either_city_data)

     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.824    94.87 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.613    90.71 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.655    91.54 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.605    90.56 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    4.581    90.08 |

either_city_data_fixed

select_variables_from_random_forest(either_city_data_fixed)

 [1] "either_pool_size"                                        "population_growth"                                       "region_100km_cultivated"                                
 [4] "region_20km_average_pop_density"                         "realm"                                                   "region_50km_cultivated"                                 
 [7] "region_50km_average_pop_density"                         "biome_name"                                              "shrubs"                                                 
[10] "rainfall_monthly_min"                                    "region_100km_average_pop_density"                        "permanent_water"                                        
[13] "region_20km_cultivated"                                  "temperature_monthly_min"                                 "region_50km_elevation_delta"                            
[16] "region_20km_urban"                                       "mean_population_exposure_to_pm2_5_2019"                  "region_20km_elevation_delta"                            
[19] "percentage_urban_area_as_open_public_spaces"             "city_average_pop_density"                                "happiness_future_life"                                  
[22] "rainfall_monthly_max"                                    "temperature_annual_average"                              "temperature_monthly_max"                                
[25] "region_100km_urban"                                      "region_50km_urban"                                       "cultivated"                                             
[28] "share_of_population_within_400m_of_open_space"           "city_max_pop_density"                                    "city_elevation_delta"                                   
[31] "city_mean_elevation"                                     "herbaceous_wetland"                                      "rainfall_annual_average"                                
[34] "region_100km_elevation_delta"                            "city_gdp_per_population"                                 "region_20km_mean_elevation"                             
[37] "percentage_urban_area_as_open_public_spaces_and_streets" "region_50km_mean_elevation"                              "region_100km_mean_elevation"                            
[40] "happiness_negative_effect"                               "open_forest"                                             "urban"                                                  
[43] "happiness_positive_effect"                               "herbaceous_vegetation"                                   "percentage_urban_area_as_streets"                       
[46] "closed_forest"                                          

select_variables_from_random_forest(either_city_data_fixed_single_scale)

 [1] "either_pool_size"                                        "population_growth"                                       "region_100km_cultivated"                                
 [4] "region_20km_average_pop_density"                         "realm"                                                   "biome_name"                                             
 [7] "rainfall_monthly_min"                                    "shrubs"                                                  "temperature_monthly_min"                                
[10] "permanent_water"                                         "percentage_urban_area_as_open_public_spaces"             "region_20km_urban"                                      
[13] "region_50km_elevation_delta"                             "mean_population_exposure_to_pm2_5_2019"                  "city_average_pop_density"                               
[16] "rainfall_monthly_max"                                    "happiness_future_life"                                   "cultivated"                                             
[19] "share_of_population_within_400m_of_open_space"           "city_elevation_delta"                                    "city_max_pop_density"                                   
[22] "rainfall_annual_average"                                 "city_mean_elevation"                                     "percentage_urban_area_as_open_public_spaces_and_streets"
[25] "temperature_monthly_max"                                 "region_20km_mean_elevation"                              "open_forest"                                            
[28] "happiness_negative_effect"                               "urban"                                                   "percentage_urban_area_as_streets"                       
[31] "closed_forest"                                          

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size")])

[1] "Mean  4.69133292981281 , SD:  0.0489285631824466 , Mean + SD:  4.74026149299525"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth")])

[1] "Mean  4.18668186942246 , SD:  0.0652412455486544 , Mean + SD:  4.25192311497111"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated")])

[1] "Mean  4.08678736410676 , SD:  0.05782944992901 , Mean + SD:  4.14461681403577"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density")])

[1] "Mean  3.7612122389608 , SD:  0.0626910363388465 , Mean + SD:  3.82390327529965"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm")])

[1] "Mean  3.62757829197622 , SD:  0.0641414031735027 , Mean + SD:  3.69171969514973"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name")])

[1] "Mean  3.88541888033384 , SD:  0.0697761912903623 , Mean + SD:  3.9551950716242"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min")])

[1] "Mean  3.94442754211899 , SD:  0.0705357146629719 , Mean + SD:  4.01496325678196"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs")])

[1] "Mean  3.94268869246952 , SD:  0.0683286721896993 , Mean + SD:  4.01101736465922"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min")])

[1] "Mean  3.91638012322931 , SD:  0.058327290718483 , Mean + SD:  3.97470741394779"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water")])

[1] "Mean  3.97807710896339 , SD:  0.0819372375299245 , Mean + SD:  4.06001434649332"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces")])

[1] "Mean  4.08096128871886 , SD:  0.0742590817487434 , Mean + SD:  4.15522037046761"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban")])

[1] "Mean  4.10276372102361 , SD:  0.0793013544355574 , Mean + SD:  4.18206507545916"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta")])

[1] "Mean  4.14360043109383 , SD:  0.0571122481729091 , Mean + SD:  4.20071267926674"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019")])

[1] "Mean  4.17420426151807 , SD:  0.0800438347270345 , Mean + SD:  4.25424809624511"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density")])

[1] "Mean  4.20142417235852 , SD:  0.0534400086677398 , Mean + SD:  4.25486418102626"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max")])

[1] "Mean  4.23495161199434 , SD:  0.0810189022929709 , Mean + SD:  4.31597051428731"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life")])

[1] "Mean  4.25511343099509 , SD:  0.0777504142418531 , Mean + SD:  4.33286384523695"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life", "cultivated")])

[1] "Mean  4.27187737013208 , SD:  0.0659717347812921 , Mean + SD:  4.33784910491338"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life", "cultivated", "share_of_population_within_400m_of_open_space")])

[1] "Mean  4.32785556380064 , SD:  0.0915334527831198 , Mean + SD:  4.41938901658376"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life", "cultivated", "share_of_population_within_400m_of_open_space", "city_elevation_delta")])

[1] "Mean  4.33688765161498 , SD:  0.0687985208297185 , Mean + SD:  4.4056861724447"

create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life", "cultivated", "share_of_population_within_400m_of_open_space", "city_elevation_delta", "city_max_pop_density")])

[1] "Mean  4.35933973615371 , SD:  0.0853454117772923 , Mean + SD:  4.444685147931"

“either_pool_size”, “population_growth”, “region_100km_cultivated”, “region_20km_average_pop_density”, “realm”

both_city_data <- fetch_city_data_for('both')

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  name = col_character(),
  response = col_double()
)

Joining, by = "name"

both_city_data

both_city_data_fixed <- rfImpute(response ~ ., both_city_data)

     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    16.32    99.56 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    15.46    94.36 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    15.88    96.87 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    15.65    95.52 |
     |      Out-of-bag   |
Tree |      MSE  %Var(y) |
 300 |    15.91    97.08 |

both_city_data_fixed

select_variables_from_random_forest(both_city_data_fixed)

 [1] "both_pool_size"                                          "temperature_annual_average"                              "temperature_monthly_min"                                
 [4] "permanent_water"                                         "happiness_negative_effect"                               "region_20km_urban"                                      
 [7] "region_100km_cultivated"                                 "region_50km_cultivated"                                  "realm"                                                  
[10] "region_20km_cultivated"                                  "rainfall_monthly_min"                                    "region_50km_elevation_delta"                            
[13] "population_growth"                                       "shrubs"                                                  "region_100km_elevation_delta"                           
[16] "region_20km_average_pop_density"                         "region_100km_urban"                                      "biome_name"                                             
[19] "region_20km_elevation_delta"                             "region_50km_urban"                                       "percentage_urban_area_as_open_public_spaces"            
[22] "city_average_pop_density"                                "city_gdp_per_population"                                 "region_50km_average_pop_density"                        
[25] "open_forest"                                             "herbaceous_wetland"                                      "cultivated"                                             
[28] "region_100km_average_pop_density"                        "region_20km_mean_elevation"                              "share_of_population_within_400m_of_open_space"          
[31] "mean_population_exposure_to_pm2_5_2019"                  "city_elevation_delta"                                    "region_50km_mean_elevation"                             
[34] "happiness_future_life"                                   "happiness_positive_effect"                               "rainfall_monthly_max"                                   
[37] "percentage_urban_area_as_open_public_spaces_and_streets" "herbaceous_vegetation"                                   "temperature_monthly_max"                                
[40] "percentage_urban_area_as_streets"                        "rainfall_annual_average"                                 "urban"                                                  
[43] "closed_forest"                                          

select_variables_from_random_forest(both_city_data_fixed_single_scale)

 [1] "both_pool_size"                                "temperature_annual_average"                    "temperature_monthly_min"                      
 [4] "permanent_water"                               "happiness_negative_effect"                     "region_20km_urban"                            
 [7] "rainfall_monthly_min"                          "realm"                                         "region_100km_cultivated"                      
[10] "region_50km_elevation_delta"                   "population_growth"                             "percentage_urban_area_as_open_public_spaces"  
[13] "shrubs"                                        "biome_name"                                    "region_20km_average_pop_density"              
[16] "city_mean_elevation"                           "city_gdp_per_population"                       "share_of_population_within_400m_of_open_space"
[19] "cultivated"                                    "open_forest"                                   "region_20km_mean_elevation"                   
[22] "rainfall_monthly_max"                          "temperature_monthly_max"                       "rainfall_annual_average"                      
[25] "percentage_urban_area_as_streets"              "closed_forest"                                 "urban"                                        

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size")])

[1] "Mean  17.0501746482574 , SD:  0.180832956779973 , Mean + SD:  17.2310076050374"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average")])

[1] "Mean  14.1770117377572 , SD:  0.12741540946317 , Mean + SD:  14.3044271472204"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min")])

[1] "Mean  13.9947226485825 , SD:  0.193418308082352 , Mean + SD:  14.1881409566648"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water")])

[1] "Mean  13.980419606819 , SD:  0.198277385993035 , Mean + SD:  14.1786969928121"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect")])

[1] "Mean  14.250869948595 , SD:  0.205833621996072 , Mean + SD:  14.4567035705911"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban")])

[1] "Mean  13.831253031623 , SD:  0.267164827147086 , Mean + SD:  14.0984178587701"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min")])

[1] "Mean  14.0176076470363 , SD:  0.233933915226803 , Mean + SD:  14.2515415622631"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm")])

[1] "Mean  13.9678429656754 , SD:  0.27500268619645 , Mean + SD:  14.2428456518719"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated"),])

[1] "Mean  13.8133557218721 , SD:  0.208047207294211 , Mean + SD:  14.0214029291663"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta"),])

[1] "Mean  14.1587620560898 , SD:  0.265407381711604 , Mean + SD:  14.4241694378014"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth"),])

[1] "Mean  14.3695231260649 , SD:  0.277971868419252 , Mean + SD:  14.6474949944841"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces"),])

[1] "Mean  14.6989193629512 , SD:  0.281371086172517 , Mean + SD:  14.9802904491237"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs"),])

[1] "Mean  14.6255340760297 , SD:  0.287689760117654 , Mean + SD:  14.9132238361473"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name"),])

[1] "Mean  14.6561287164134 , SD:  0.265987324658439 , Mean + SD:  14.9221160410718"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name", "region_20km_average_pop_density"),])

[1] "Mean  14.7323907720158 , SD:  0.194408848363496 , Mean + SD:  14.9267996203793"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name", "region_20km_average_pop_density", "city_mean_elevation"),])

[1] "Mean  14.7915916869301 , SD:  0.298364380477512 , Mean + SD:  15.0899560674076"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name", "region_20km_average_pop_density", "city_mean_elevation", "city_gdp_per_population"),])

[1] "Mean  14.9134394356877 , SD:  0.224050531561373 , Mean + SD:  15.137489967249"

create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name", "region_20km_average_pop_density", "city_mean_elevation", "city_gdp_per_population", "share_of_population_within_400m_of_open_space"),])

[1] "Mean  15.0099497484692 , SD:  0.2218461976947 , Mean + SD:  15.2317959461639"

“both_pool_size”, “temperature_annual_average”, “happiness_negative_effect”

So….
“merlin_pool_size”, “realm” “population_growth”, “birdlife_pool_size”, “region_100km_cultivated”, “percentage_urban_area_as_open_public_spaces”, “biome_name”, “rainfall_monthly_min”, “region_20km_average_pop_density”, “permanent_water” “either_pool_size”, “population_growth”, “region_100km_cultivated”, “region_20km_average_pop_density”, “realm” “both_pool_size”, “temperature_annual_average”, “temperature_monthly_min”
```r summary(lm(response ~ merlin_pool_size, merlin_city_data_fixed))
```
```
Call: lm(formula = response ~ merlin_pool_size, data = merlin_city_data_fixed)
Residuals: Min 1Q Median 3Q Max -8.3644 -2.2493 -0.3649 1.7804 15.4604
Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.205975 0.920945 6.739 4.23e-10 merlin_pool_size -0.022439 0.003134 -7.160 4.71e-11

Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.641 on 135 degrees of freedom Multiple R-squared: 0.2752, Adjusted R-squared: 0.2698 F-statistic: 51.26 on 1 and 135 DF, p-value: 4.707e-11

<!-- rnb-output-end -->

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```r
summary(lm(response ~ birdlife_pool_size, birdlife_city_data_fixed))

Call:
lm(formula = response ~ birdlife_pool_size, data = birdlife_city_data_fixed)

Residuals:
   Min     1Q Median     3Q    Max 
-5.140 -1.330 -0.313  1.034  9.156 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         2.602931   0.625873   4.159 5.65e-05 ***
birdlife_pool_size -0.008789   0.002000  -4.395 2.23e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.368 on 135 degrees of freedom
Multiple R-squared:  0.1252,    Adjusted R-squared:  0.1187 
F-statistic: 19.31 on 1 and 135 DF,  p-value: 2.225e-05

summary(lm(response ~ either_pool_size, either_city_data_fixed))

Call:
lm(formula = response ~ either_pool_size, data = either_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8488 -1.0658 -0.3811  0.8665  6.5921 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)       3.250304   0.584389   5.562 1.38e-07 ***
either_pool_size -0.009005   0.001546  -5.825 3.99e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.031 on 135 degrees of freedom
Multiple R-squared:  0.2008,    Adjusted R-squared:  0.1949 
F-statistic: 33.92 on 1 and 135 DF,  p-value: 3.99e-08

summary(lm(response ~ both_pool_size, both_city_data_fixed))

Call:
lm(formula = response ~ both_pool_size, data = both_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.9674 -2.7370 -0.3475  1.8439 10.3398 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     5.261657   0.982371   5.356 3.56e-07 ***
both_pool_size -0.024842   0.004396  -5.651 9.08e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.667 on 135 degrees of freedom
Multiple R-squared:  0.1913,    Adjusted R-squared:  0.1853 
F-statistic: 31.94 on 1 and 135 DF,  p-value: 9.076e-08

summary(lm(response ~ region_100km_cultivated, merlin_city_data_fixed))

Call:
lm(formula = response ~ region_100km_cultivated, data = merlin_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.7405 -2.8276 -0.5911  1.5098 18.0590 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)  
(Intercept)              -0.6281     0.5172  -1.214   0.2267  
region_100km_cultivated   2.3444     1.3805   1.698   0.0918 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.232 on 135 degrees of freedom
Multiple R-squared:  0.02092,   Adjusted R-squared:  0.01366 
F-statistic: 2.884 on 1 and 135 DF,  p-value: 0.09176

summary(lm(response ~ region_100km_cultivated, birdlife_city_data_fixed))

Call:
lm(formula = response ~ region_100km_cultivated, data = birdlife_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.4506 -1.5884 -0.3702  1.3865  9.9581 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)   
(Intercept)              -0.6226     0.3002  -2.074  0.04001 * 
region_100km_cultivated   2.3237     0.8013   2.900  0.00436 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.457 on 135 degrees of freedom
Multiple R-squared:  0.05864,   Adjusted R-squared:  0.05167 
F-statistic: 8.409 on 1 and 135 DF,  p-value: 0.004359

summary(lm(response ~ region_100km_cultivated, either_city_data_fixed))

Call:
lm(formula = response ~ region_100km_cultivated, data = either_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.6522 -1.4255 -0.2114  0.9771  6.3724 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)   
(Intercept)              -0.5459     0.2698  -2.024  0.04499 * 
region_100km_cultivated   2.0373     0.7200   2.830  0.00537 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.207 on 135 degrees of freedom
Multiple R-squared:  0.05599,   Adjusted R-squared:  0.049 
F-statistic: 8.008 on 1 and 135 DF,  p-value: 0.00537

summary(lm(response ~ region_100km_cultivated, both_city_data_fixed))

Call:
lm(formula = response ~ region_100km_cultivated, data = both_city_data_fixed)

Residuals:
   Min     1Q Median     3Q    Max 
-8.439 -2.791 -0.689  1.898 12.088 

Coefficients:
                        Estimate Std. Error t value Pr(>|t|)  
(Intercept)              -0.7221     0.4908  -1.471   0.1436  
region_100km_cultivated   2.6951     1.3099   2.057   0.0416 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.016 on 135 degrees of freedom
Multiple R-squared:  0.0304,    Adjusted R-squared:  0.02322 
F-statistic: 4.233 on 1 and 135 DF,  p-value: 0.04157

ggplot() + 
  geom_point(aes(x = merlin_pool_size, y = region_100km_cultivated), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = birdlife_pool_size, y = region_100km_cultivated), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = either_pool_size, y = region_100km_cultivated), either_city_data_fixed, color = "green") +
  geom_point(aes(x = both_pool_size, y = region_100km_cultivated), both_city_data_fixed, color = "purple")

ggplot() + 
  geom_point(aes(x = population_growth, y = response), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = population_growth, y = response), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = population_growth, y = response), either_city_data_fixed, color = "green") +
  geom_point(aes(x = population_growth, y = response), both_city_data_fixed, color = "purple")

ggplot() + 
  geom_point(aes(x = merlin_pool_size, y = region_20km_average_pop_density), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = birdlife_pool_size, y = region_20km_average_pop_density), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = either_pool_size, y = region_20km_average_pop_density), either_city_data_fixed, color = "green") +
  geom_point(aes(x = both_pool_size, y = region_20km_average_pop_density), both_city_data_fixed, color = "purple")

summary(lm(response ~ population_growth, merlin_city_data_fixed))

Call:
lm(formula = response ~ population_growth, data = merlin_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.2751 -2.8391 -0.4272  1.4837 18.4058 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)
(Intercept)        0.094091   0.524516   0.179    0.858
population_growth -0.001479   0.005915  -0.250    0.803

Residual standard error: 4.276 on 135 degrees of freedom
Multiple R-squared:  0.0004627, Adjusted R-squared:  -0.006941 
F-statistic: 0.0625 on 1 and 135 DF,  p-value: 0.803

summary(lm(response ~ population_growth, birdlife_city_data_fixed))

Call:
lm(formula = response ~ population_growth, data = birdlife_city_data_fixed)

Residuals:
   Min     1Q Median     3Q    Max 
-5.085 -1.538 -0.459  1.240 10.226 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)
(Intercept)        0.231365   0.309332   0.748    0.456
population_growth -0.003636   0.003489  -1.042    0.299

Residual standard error: 2.522 on 135 degrees of freedom
Multiple R-squared:  0.007984,  Adjusted R-squared:  0.0006359 
F-statistic: 1.087 on 1 and 135 DF,  p-value: 0.2991

summary(lm(response ~ population_growth, either_city_data_fixed))

Call:
lm(formula = response ~ population_growth, data = either_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1409 -1.3284 -0.1829  0.8324  6.7919 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)
(Intercept)        0.113195   0.278318   0.407    0.685
population_growth -0.001779   0.003139  -0.567    0.572

Residual standard error: 2.269 on 135 degrees of freedom
Multiple R-squared:  0.002374,  Adjusted R-squared:  -0.005016 
F-statistic: 0.3213 on 1 and 135 DF,  p-value: 0.5718

summary(lm(response ~ population_growth, both_city_data_fixed))

Call:
lm(formula = response ~ population_growth, data = both_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.1143 -2.5568 -0.7818  2.1289 12.4621 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)
(Intercept)        0.188410   0.499736   0.377    0.707
population_growth -0.002961   0.005636  -0.525    0.600

Residual standard error: 4.074 on 135 degrees of freedom
Multiple R-squared:  0.002041,  Adjusted R-squared:  -0.005351 
F-statistic: 0.2761 on 1 and 135 DF,  p-value: 0.6001

summary(lm(response ~ rainfall_monthly_min, merlin_city_data_fixed))

Call:
lm(formula = response ~ rainfall_monthly_min, data = merlin_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.2835 -2.9452 -0.4893  1.4983 18.2505 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)
(Intercept)           0.191459   0.491332   0.390    0.697
rainfall_monthly_min -0.007481   0.012853  -0.582    0.562

Residual standard error: 4.272 on 135 degrees of freedom
Multiple R-squared:  0.002503,  Adjusted R-squared:  -0.004886 
F-statistic: 0.3387 on 1 and 135 DF,  p-value: 0.5615

summary(lm(response ~ rainfall_monthly_min, birdlife_city_data_fixed))

Call:
lm(formula = response ~ rainfall_monthly_min, data = birdlife_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.0114 -1.4084 -0.4231  1.3632 10.6767 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)
(Intercept)           0.244199   0.289526   0.843     0.40
rainfall_monthly_min -0.009541   0.007574  -1.260     0.21

Residual standard error: 2.517 on 135 degrees of freedom
Multiple R-squared:  0.01162,   Adjusted R-squared:  0.004298 
F-statistic: 1.587 on 1 and 135 DF,  p-value: 0.2099

summary(lm(response ~ rainfall_monthly_min, either_city_data_fixed))

Call:
lm(formula = response ~ rainfall_monthly_min, data = either_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1121 -1.3720 -0.2964  0.8111  6.5298 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)
(Intercept)           0.219743   0.259756   0.846    0.399
rainfall_monthly_min -0.008586   0.006795  -1.264    0.209

Residual standard error: 2.258 on 135 degrees of freedom
Multiple R-squared:  0.01169,   Adjusted R-squared:  0.004367 
F-statistic: 1.597 on 1 and 135 DF,  p-value: 0.2086

summary(lm(response ~ rainfall_monthly_min, both_city_data_fixed))

Call:
lm(formula = response ~ rainfall_monthly_min, data = both_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-9.0991 -2.8506 -0.8491  1.9009 12.2257 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)
(Intercept)           0.30602    0.46742   0.655    0.514
rainfall_monthly_min -0.01196    0.01223  -0.978    0.330

Residual standard error: 4.064 on 135 degrees of freedom
Multiple R-squared:  0.007033,  Adjusted R-squared:  -0.0003223 
F-statistic: 0.9562 on 1 and 135 DF,  p-value: 0.3299

ggplot() + 
  geom_point(aes(x = rainfall_monthly_min, y = response), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = rainfall_monthly_min, y = response), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = rainfall_monthly_min, y = response), either_city_data_fixed, color = "green") +
  geom_point(aes(x = rainfall_monthly_min, y = response), both_city_data_fixed, color = "purple")

ggplot() + 
  geom_point(aes(x = temperature_annual_average, y = response), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = temperature_annual_average, y = response), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = temperature_annual_average, y = response), either_city_data_fixed, color = "green") +
  geom_point(aes(x = temperature_annual_average, y = response), both_city_data_fixed, color = "purple")

ggplot() + 
  geom_point(aes(x = percentage_urban_area_as_open_public_spaces, y = response), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = percentage_urban_area_as_open_public_spaces, y = response), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = percentage_urban_area_as_open_public_spaces, y = response), either_city_data_fixed, color = "green") +
  geom_point(aes(x = percentage_urban_area_as_open_public_spaces, y = response), both_city_data_fixed, color = "purple")

ggplot() + 
  geom_point(aes(x = happiness_negative_effect, y = response), merlin_city_data_fixed, color = "red") +
  geom_point(aes(x = happiness_negative_effect, y = response), birdlife_city_data_fixed, color = "blue") +
  geom_point(aes(x = happiness_negative_effect, y = response), either_city_data_fixed, color = "green") +
  geom_point(aes(x = happiness_negative_effect, y = response), both_city_data_fixed, color = "purple")

ggplot() + 
  geom_boxplot(aes(x = response, y = biome_name), merlin_city_data_fixed)

ggplot() + 
  geom_boxplot(aes(x = response, y = biome_name), birdlife_city_data_fixed)

ggplot() + 
  geom_boxplot(aes(x = response, y = biome_name), either_city_data_fixed)

ggplot() + 
  geom_boxplot(aes(x = response, y = biome_name), both_city_data_fixed)

summary(lm(response ~ biome_name, merlin_city_data_fixed))

Call:
lm(formula = response ~ biome_name, data = merlin_city_data_fixed)

Residuals:
    Min      1Q  Median      3Q     Max 
-8.7663 -2.4594 -0.4676  2.1272 18.4309 

Coefficients:
                                                                   Estimate Std. Error t value Pr(>|t|)  
(Intercept)                                                         -3.2599     4.2666  -0.764   0.4463  
biome_nameDeserts & Xeric Shrublands                                 3.1836     4.4563   0.714   0.4763  
biome_nameFlooded Grasslands & Savannas                              0.6618     5.2255   0.127   0.8994  
biome_nameMangroves                                                  9.3150     5.2255   1.783   0.0771 .
biome_nameMediterranean Forests, Woodlands & Scrub                   3.2643     4.4066   0.741   0.4602  
biome_nameMontane Grasslands & Shrublands                            1.5344     5.2255   0.294   0.7695  
biome_nameTemperate Broadleaf & Mixed Forests                        3.2942     4.3328   0.760   0.4485  
biome_nameTemperate Conifer Forests                                  3.3572     5.2255   0.642   0.5218  
biome_nameTemperate Grasslands, Savannas & Shrublands                4.3835     4.6739   0.938   0.3501  
biome_nameTropical & Subtropical Coniferous Forests                  7.4846     5.2255   1.432   0.1546  
biome_nameTropical & Subtropical Dry Broadleaf Forests               3.7631     4.4164   0.852   0.3958  
biome_nameTropical & Subtropical Grasslands, Savannas & Shrublands   5.9138     4.6739   1.265   0.2081  
biome_nameTropical & Subtropical Moist Broadleaf Forests             2.4622     4.3148   0.571   0.5693  
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Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 4.267 on 124 degrees of freedom
Multiple R-squared:  0.08597,   Adjusted R-squared:  -0.002489 
F-statistic: 0.9719 on 12 and 124 DF,  p-value: 0.4793

In Summary

Response is related to number of species in regional pool, the more species, the less the percentage of species in the city. Indicating a fixed number of species are able to move into cities. The size of the regional pool is correlated with both the amount of urban and cultivated land cover, both reduce species in the regional pool.

Response is also lower in wet biomes and areas of the world, this is seen through the higher rainfall in the month with least rainfall in the year, and lower percentages in wetter biomes such as flooded grasslands and moist broadleaf forests.

Finally cities with a higher proportion of green public space are less likely to have a low response.